This topic focuses on the development of new methodologies using multi-scale and multi-sensor hyperspectral remote sensing (from the laboratory and field operations to airborne and satellite applications) for geosciences and environmental applications.
Our group focuses on the development of future EO sensors and state of the art data pre-processing algorithms. Our activities cover a broad spectrum of research fields such as hyperspectral sensors, sensor end-to-end simulations, night illumination, radiative transfer, atmospheric correction, big data processing, as well as geometric fusion of hyperspectral and Lidar data.
The research of the GFZ Section for Remote Sensing and Geoinformatics within the TERENO working group focuses on the monitoring of surface parameters, based on the synergetic use of ground-based in-situ measurements from climate and soil moisture measuring stations with remote sensing based analyses. The focus is on the long-term monitoring of mainly agricultural areas.
Our working group deals with the development and application of remote sensing methods using radar and optical data for a comprehensive understanding of natural and anthropogenic geohazards. Our primary goal consists in obtaining an improved spatiotemporal process understanding as an important basis for improved hazard and risk assessment. Methodological developments are based on optical as well as on radar data aiming at their combined use for a best possible information extraction.
The Earth-Atmosphere Interactions Group has evolved from the Helmholtz Young Investigators Group TEAM: "Trace Gas Exchange in the Earth-Atmosphere System on Multiple Scales".
Young Investigator Group Leader: Prof. Torsten Sachs
The working group develops concepts and software tools to analyse and interprete large, high-dimensional, heterogeneous and often uncertain data sets. It combines methods from data mining/machine learning, visual data exploration, data bases and big data technologies into novel approaches.
Visual analysis methods to validate simulation models against imprecise terrestrial observations